Search results

Filter trading is a technical trading rule that has been used extensively to test the efficient market hypothesis in the context of long-term trading. In this paper, the authors adopt the rule to analyze intraday trading, in which an open position is not left overnight. This paper aims to explore the relationship between intraday filter trading profitability and intraday realized volatilities. The bivariate thin plate spline (TPS) model is chosen to fit the predictor-response surface for high frequency data from the Hang Seng index futures (HSIF) market. The hypotheses follow the adaptive market hypothesis, arguing that intraday filter trading differs in profitability under different market conditions as measured by realized volatility, and furthermore, the optimal filter size for trading on each day is related to the realized volatility. The empirical results furnish new evidence that range-based realized volatilities (RaV) are more efficient in identifying trading profit than return-based volatilities (ReV). These results shed light on the efficiency of intraday high frequency trading in the HSIF market. Some trading suggestions are given based on the findings.

Among all the factors that affect the profit of filter trading, intraday realized volatility stands out as an important predictor. The authors explore several intraday volatilities measures using range-based or return-based methods of estimation. The authors then study how the filter trading profit will depend on realized volatility and how the optimal filter size is related to the realized volatility. The bivariate TPS model is used to model the predictor-response relationship.

The empirical results show that range-based realized volatility has a higher predictive power on filter rule trading profit than the return-based realized volatility.

First, the authors contribute to the literature by investigating the profitability of the filter trading rule on high frequency tick-by-tick data of HSIF market. Second, the authors test the assumption that the magnitude of the intraday momentum trading profit depends on the realized volatilities and aims to identify a relationship between them. Furthermore, the authors consider several intraday realized volatilities and find the RaV have the higher prediction power than ReV. Finally, the authors find some relationship between the optimal filter size and the realized volatilities. Based on the observations, the authors also give some trading suggestions to the intraday filter traders.

The purpose of this research is to consider whether market wide herding occurs intraday.

Using the 1995 Christie and Huang and the 2000 Chang et al. models, the paper tests whether market wide and industry sector herding occurs intraday in the Australian equities market.

Neither market wide nor industry sector herding occurs intraday.

Both herding measures focus on one specific type of herding, herding evidenced by changes in the cross‐sectional return distribution. Therefore the herding measures are ill suited to capture the effects of period specific abnormally high or low market returns and they can also capture herding of market participants or groups of market participants only in as far as it manifests itself in security specific returns.

No previous studies have considered the possibility of intraday herding in equities markets. Even if there is little evidence of herding over longer time periods, market frictions and inefficiencies continue to be exploited at least anecdotally by traders with very short time horizons to the detriment of longer term investors.

The purpose of this paper is to investigate the intraday effect of nature disaster (external inevitable factor) and production safety accident (PSA) (internal factor regarding management level) announcement on stock price in China’s stock markets.

Using high-frequency data, this study adopts event study method to examine the intraday abnormal returns as well as the volatility of stock price before and after the announcement of nature disaster and PSA.

First, both nature disaster announcement and PSA announcement produce negative effects on stock returns. However, there are some differences in effects between the different types of announcement. Second, it is just within the event day (announcement day if trading day, otherwise the first trading day after announcement) that the volatility of stock price is distinctly increased by the two kinds of announcement. Third, there are some differences in the impacts of nature disaster announcement on firms in different industries. Finally, there are also some differences observed between the impacts of PSA announcement on chemical firms and other firms.

It is the first time that using high-frequency data to analyze the intraday impact of nature disaster and PSA announcement on stock short price behavior. The results can help us to understand the role of market microstructure playing in the process of stock price formation, especially the stock price movements before and after disaster and accident announcement and the sensitivity to the announcement. The empirical results have important implications for investors when making trading decisions, and for market regulators when setting trading rules.

This study empirically explores the intraday price discovery mechanism and volatility transmission effect between the dual-listed Indian Nifty index futures traded simultaneously on the onshore Indian exchange, National Stock Exchange (NSE) and offshore Singapore Exchange (SGX) and its spot market by using high-frequency data.

This study applies the vector error correction model to analyze the lead-lag relationship in price discovery among three markets. The contributions of individual markets in assimilating new information into prices are measured using various measures, Hasbrouck's (1995) information share, Lien and Shrestha's (2009) modified information share and Gonzalo and Granger's (1995) component share. Additionally, the Granger causality test is conducted to determine the causal relationship. Lastly, the BEKK-GARCH specification is employed to analyze the volatility transmission.

This study provides robust evidence that Nifty futures lead the spot in price discovery. The offshore SGX Nifty futures consistently ranked first in contributing to price discovery, followed by onshore NSE Nifty futures and finally by the spot. Empirical results also show unidirectional causality and volatility transmission from Nifty futures to spot, as well as bidirectional causal relationship and volatility spillovers between NSE and SGX Nifty futures. These novel findings provide fresh insights into the informational efficiency of the dual-listed Indian Nifty futures, which is distinct from previous literature.

These findings can potentially help market participants, policymakers, stock exchanges and regulators.

Unlike previous studies in this area, this is the first study that empirically examines the intraday price discovery mechanism and volatility spillover between the dual-listed futures markets and its spot market using 5-min overlapping price data and trivariate econometric models.

The purpose of this paper is to study short sales trading as part of the New York Stock Exchange (NYSE) batch open and National Association of Securities Dealers Automated Quotations (NASDAQ) opening cross. The paper examines whether short transactions at the open can predict future returns.

The study tests to see if short transactions in the NYSE opening batch trade and NASDAQ opening cross are informative of future returns.

It is found that a stock's opening‐trade short volume is predictive of its short volume for the rest of trading day, positively related to its previous‐day price change, and positively related to its overnight price change at the opening trade on option‐expiration Fridays when the stock is part of the Standard and Poor (S and P) 500 index.

While previous research shows that intraday short sale trades are informative, this is the first paper to examine the opening trade of the day, and whether these short sales are informative.

We examine the impact of the unique Japanese stock market microstructure on the pricing of stock index futures contracts. We use intraday transactions data for the Nikkei 225 Futures contracts in Osaka and the corresponding Nikkei 225 Index in Tokyo. Incorporating more realistic transaction-cost estimates and various institutional impediments in Japan, we find that the time-varying liquidity of some component shares of the index in Tokyo represents the most critical impediment to intraday arbitrage and often causes futures prices in Osaka to deviate significantly and persistently from their no-arbitrage boundary, especially for longer-lived contracts.

This paper aims to look at the effects of the closing call auction on market quality and behavior by using the natural experiment of its introduction at the Abu Dhabi Stock Exchange.

Current paper studies the effect of closing call auction on various market quality factors such as liquidity, bid-ask spreads, volatility and market efficiency. Liquidity is proxied by trading volume. Bid-ask spreads provide a measure for the cost of trading in the market. Volatility is measured by using Parkinson’s (1980) volatility as in Huang and Tsai (2008). Last but not least, efficiency will be obtained by estimating a relative return dispersion measure as in Huang and Tsai (2008).

The introduction of the closing call auction leads to a significant decrease in the trading volume toward the end of the continuous trading. At the same time, trading activity taking place during the call auction significantly increases. This implies a redistribution of liquidity. The implementation of the closing call auction also improves market quality by reducing market inefficiency in terms of firm-specific noise. The study also documents that there exists no significant change in the cost of trading and intraday volatility in the post-period following the adoption of closing call auction.

This current study is the first one looking at this topic for the Abu Dhabi Stock Exchange. Specifically, this paper looks at the changes in trading volume, bid-ask spreads, intraday return volatility and market efficiency after the implementation of the closing call mechanism.

In this chapter, I examine the properties of four realized correlation estimators and model their jumps. The correlations are between the three main FTSE indices of the Athens Stock Exchange. Using intraday data I first construct four state-of-the-art realized correlation estimators which I then use in testing for normality, long memory, asymmetries and jumps and also in modelling for jumps. Jumps are detected when the realized correlation is higher than 0.99 and lower than 0.01 in absolute values. Then the realized correlation is modelled with the simple heterogeneous autoregressive (HAR) model and the HAR model with jumps (HAR-J). This is the first time, to the best of my knowledge, that the realized correlation between the three indices for the Greek equity market is examined.